Sentence examples for multiple interaction networks from inspiring English sources

Based on these results, we believe that the methodology developed in this study is capable of identifying common and frequent subgraphs from large and multiple interaction networks.

A basic implementation of this concept takes as input multiple interaction networks along with the similarity relationships between proteins from different species.

For this we used Genes2Networks, which integrates the data contained in multiple interaction network datasets, enables the user to determine the path length (degree of interaction), and incorporates possible interacting proteins which are not part of the user's original input list.

Another area of significant progress is multiple network alignment tools, e.g. Grælin developed by Flannick et al. [ 30], which uses a probabilistic function for topology matching, and can be applied to search for conserved functional modules among multiple protein interaction networks.

Zhang et al. extend the regression model to include multiple protein-protein interaction networks [ 19].

When performing comparative analysis on multiple protein-protein interaction networks, we define distinct modules as the functional modules that exist exclusively in a subset of protein-protein interaction networks.

In flexible proteins, the amino acid side chains can form multiple interchangeable non-bonded interaction networks, corresponding to iso-energetic minima in the energy landscape.

This method compares protein interaction networks across multiple species to detect evolutionarily and functionally conserved subgraphs.

Thus, several methods have been used to reconstitute interaction networks from multiple published datasets.

The results demonstrated that our algorithm is substantially useful to decipher gene interaction networks from multiple time-course gene expression data, especially for less studied organisms where little knowledge is available except gene expression data.

In light of this, we performed a search to see if any single TFs played central roles in controlling tissue gene expression across different tissues, and looked for internal TFs in multiple tissue-type TF-TF interaction networks.